Overview

Dataset statistics

Number of variables21
Number of observations1246
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory654.6 KiB
Average record size in memory538.0 B

Variable types

NUM15
CAT5
UNSUPPORTED1

Reproduction

Analysis started2021-03-06 12:29:46.997948
Analysis finished2021-03-06 12:30:20.247348
Duration33.25 seconds
Versionpandas-profiling v2.7.1
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml
title has a high cardinality: 1238 distinct values High cardinality
deltaMedianPrice is highly correlated with deltaAvgPriceHigh correlation
deltaAvgPrice is highly correlated with deltaMedianPriceHigh correlation
dublinNorthSouth is highly correlated with neighbourhoodHigh correlation
neighbourhood is highly correlated with dublinNorthSouthHigh correlation
title is uniformly distributed Uniform
df_index has unique values Unique
floorArea is an unsupported type, check if it needs cleaning or further analysis Unsupported
deltaMedianPrice has 50 (4.0%) zeros Zeros

Variables

df_index
Real number (ℝ≥0)

UNIQUE
Distinct count1246
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1439.1958266452648
Minimum2
Maximum2904
Zeros0
Zeros (%)0.0%
Memory size9.9 KiB
2021-03-06T12:30:20.316075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile184.5
Q1802.25
median1453.5
Q32067.25
95-th percentile2703.25
Maximum2904
Range2902
Interquartile range (IQR)1265

Descriptive statistics

Standard deviation778.831748
Coefficient of variation (CV)0.541157592
Kurtosis-1.038071287
Mean1439.195827
Median Absolute Deviation (MAD)635
Skewness0.0174243834
Sum1793238
Variance606578.8917
2021-03-06T12:30:20.417389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2092 1 0.1%
 
1297 1 0.1%
 
1311 1 0.1%
 
1310 1 0.1%
 
1309 1 0.1%
 
1307 1 0.1%
 
1305 1 0.1%
 
1302 1 0.1%
 
1299 1 0.1%
 
1295 1 0.1%
 
Other values (1236) 1236 99.2%
 
ValueCountFrequency (%) 
2 1 0.1%
 
11 1 0.1%
 
13 1 0.1%
 
14 1 0.1%
 
15 1 0.1%
 
ValueCountFrequency (%) 
2904 1 0.1%
 
2902 1 0.1%
 
2899 1 0.1%
 
2893 1 0.1%
 
2884 1 0.1%
 

title
Categorical

HIGH CARDINALITY
UNIFORM
Distinct count1238
Unique (%)99.4%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
Apartment 101, Milltown Hall, Milltown Avenue, Mount Saint Annes, Milltown, Dublin 6
 
2
1 Fitzgibbon Lane, Dublin 1
 
2
20 Muckross Green, Perrystown, Dublin 12
 
2
20 Moyclare Road, Baldoyle, Dublin 13
 
2
88 Scholarstown Park, Rathfarnham, Dublin 16
 
2
Other values (1233)
1236
ValueCountFrequency (%) 
Apartment 101, Milltown Hall, Milltown Avenue, Mount Saint Annes, Milltown, Dublin 6 2 0.2%
 
1 Fitzgibbon Lane, Dublin 1 2 0.2%
 
20 Muckross Green, Perrystown, Dublin 12 2 0.2%
 
20 Moyclare Road, Baldoyle, Dublin 13 2 0.2%
 
88 Scholarstown Park, Rathfarnham, Dublin 16 2 0.2%
 
Apartment 427, The Old Chocolate Factory, Kilmainham Square, Kilmainham, Dublin 8 2 0.2%
 
171 Drimnagh Road, Drimnagh, Dublin 12 2 0.2%
 
24 The Wood, Millbrook Lawns, Tallaght, Dublin 24 2 0.2%
 
Apartment 15, Blackhall Court, Stoneybatter, Dublin 7 1 0.1%
 
193 Annamoe Drive, Cabra, Dublin 7 1 0.1%
 
Other values (1228) 1228 98.6%
 
2021-03-06T12:30:20.532638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length84
Mean length45.20545746
Min length22
ValueCountFrequency (%) 
Lowercase_Letter 27 38.0%
 
Uppercase_Letter 25 35.2%
 
Decimal_Number 10 14.1%
 
Other_Punctuation 4 5.6%
 
Open_Punctuation 1 1.4%
 
Connector_Punctuation 1 1.4%
 
Dash_Punctuation 1 1.4%
 
Close_Punctuation 1 1.4%
 
Space_Separator 1 1.4%
 
ValueCountFrequency (%) 
Latin 52 73.2%
 
Common 19 26.8%
 
ValueCountFrequency (%) 
ASCII 70 100.0%
 

neighbourhood
Categorical

HIGH CORRELATION
Distinct count22
Unique (%)1.8%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
Dublin 15
 
129
Dublin 11
 
96
Dublin 8
 
91
Dublin 3
 
76
Dublin 4
 
75
Other values (17)
779
ValueCountFrequency (%) 
Dublin 15 129 10.4%
 
Dublin 11 96 7.7%
 
Dublin 8 91 7.3%
 
Dublin 3 76 6.1%
 
Dublin 4 75 6.0%
 
Dublin 14 71 5.7%
 
Dublin 7 70 5.6%
 
Dublin 9 70 5.6%
 
Dublin 24 69 5.5%
 
Dublin 6 66 5.3%
 
Other values (12) 433 34.8%
 
2021-03-06T12:30:20.645468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length9
Mean length8.543338684
Min length8
ValueCountFrequency (%) 
Decimal_Number 10 55.6%
 
Lowercase_Letter 5 27.8%
 
Uppercase_Letter 2 11.1%
 
Space_Separator 1 5.6%
 
ValueCountFrequency (%) 
Common 11 61.1%
 
Latin 7 38.9%
 
ValueCountFrequency (%) 
ASCII 18 100.0%
 

propertyType
Categorical

Distinct count9
Unique (%)0.7%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
Apartment
388
Terrace
335
Semi-D
267
End of Terrace
112
Detached
 
65
Other values (4)
79
ValueCountFrequency (%) 
Apartment 388 31.1%
 
Terrace 335 26.9%
 
Semi-D 267 21.4%
 
End of Terrace 112 9.0%
 
Detached 65 5.2%
 
Duplex 33 2.6%
 
Bungalow 20 1.6%
 
Townhouse 13 1.0%
 
Site 13 1.0%
 
2021-03-06T12:30:20.755014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length14
Mean length8.069020867
Min length4
ValueCountFrequency (%) 
Lowercase_Letter 19 70.4%
 
Uppercase_Letter 6 22.2%
 
Space_Separator 1 3.7%
 
Dash_Punctuation 1 3.7%
 
ValueCountFrequency (%) 
Latin 25 92.6%
 
Common 2 7.4%
 
ValueCountFrequency (%) 
ASCII 27 100.0%
 

numBedrooms
Real number (ℝ)

Distinct count9
Unique (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.601123595505618
Minimum-1.0
Maximum11.0
Zeros0
Zeros (%)0.0%
Memory size9.9 KiB
2021-03-06T12:30:20.844127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum11
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.011880601
Coefficient of variation (CV)0.3890167322
Kurtosis5.195947532
Mean2.601123596
Median Absolute Deviation (MAD)1
Skewness0.4453117177
Sum3241
Variance1.023902351
2021-03-06T12:30:20.920857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3 512 41.1%
 
2 448 36.0%
 
4 123 9.9%
 
1 108 8.7%
 
5 37 3.0%
 
-1 13 1.0%
 
6 2 0.2%
 
7 2 0.2%
 
11 1 0.1%
 
ValueCountFrequency (%) 
-1 13 1.0%
 
1 108 8.7%
 
2 448 36.0%
 
3 512 41.1%
 
4 123 9.9%
 
ValueCountFrequency (%) 
11 1 0.1%
 
7 2 0.2%
 
6 2 0.2%
 
5 37 3.0%
 
4 123 9.9%
 

numBathrooms
Real number (ℝ)

Distinct count7
Unique (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6404494382022472
Minimum-1.0
Maximum6.0
Zeros0
Zeros (%)0.0%
Memory size9.9 KiB
2021-03-06T12:30:21.000430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum6
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8553734469
Coefficient of variation (CV)0.5214262792
Kurtosis1.742502726
Mean1.640449438
Median Absolute Deviation (MAD)1
Skewness0.6844891597
Sum2044
Variance0.7316637336
2021-03-06T12:30:21.087311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1 619 49.7%
 
2 429 34.4%
 
3 152 12.2%
 
4 24 1.9%
 
-1 16 1.3%
 
5 5 0.4%
 
6 1 0.1%
 
ValueCountFrequency (%) 
-1 16 1.3%
 
1 619 49.7%
 
2 429 34.4%
 
3 152 12.2%
 
4 24 1.9%
 
ValueCountFrequency (%) 
6 1 0.1%
 
5 5 0.4%
 
4 24 1.9%
 
3 152 12.2%
 
2 429 34.4%
 

floorArea
Unsupported

REJECTED
UNSUPPORTED
Missing0
Missing (%)0.0%
Memory size9.9 KiB

price
Real number (ℝ≥0)

Distinct count187
Unique (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean394611.5569823435
Minimum75000.0
Maximum2500000.0
Zeros0
Zeros (%)0.0%
Memory size9.9 KiB
2021-03-06T12:30:21.176060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum75000
5-th percentile195000
Q1260000
median347000
Q3450000
95-th percentile795000
Maximum2500000
Range2425000
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation221645.0652
Coefficient of variation (CV)0.5616791026
Kurtosis21.70185396
Mean394611.557
Median Absolute Deviation (MAD)97000
Skewness3.471018183
Sum491686000
Variance4.912653493e+10
2021-03-06T12:30:21.256264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
275000 43 3.5%
 
350000 36 2.9%
 
395000 33 2.6%
 
250000 33 2.6%
 
375000 32 2.6%
 
425000 31 2.5%
 
295000 29 2.3%
 
450000 28 2.2%
 
285000 27 2.2%
 
325000 27 2.2%
 
Other values (177) 927 74.4%
 
ValueCountFrequency (%) 
75000 1 0.1%
 
95000 1 0.1%
 
129000 1 0.1%
 
130000 1 0.1%
 
135000 2 0.2%
 
ValueCountFrequency (%) 
2500000 2 0.2%
 
2300000 1 0.1%
 
1800000 1 0.1%
 
1700000 1 0.1%
 
1600000 2 0.2%
 

rating
Categorical

Distinct count16
Unique (%)1.3%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
D2
169
D1
153
C3
127
C2
123
C1
 
109
Other values (11)
565
ValueCountFrequency (%) 
D2 169 13.6%
 
D1 153 12.3%
 
C3 127 10.2%
 
C2 123 9.9%
 
C1 109 8.7%
 
E1 93 7.5%
 
B3 91 7.3%
 
E2 84 6.7%
 
F 72 5.8%
 
G 70 5.6%
 
Other values (6) 155 12.4%
 
2021-03-06T12:30:21.358313image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length6
Mean length2.073033708
Min length1
ValueCountFrequency (%) 
Uppercase_Letter 10 66.7%
 
Decimal_Number 4 26.7%
 
Connector_Punctuation 1 6.7%
 
ValueCountFrequency (%) 
Latin 10 66.7%
 
Common 5 33.3%
 
ValueCountFrequency (%) 
ASCII 15 100.0%
 

sellerId
Real number (ℝ≥0)

Distinct count192
Unique (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5680.025682182985
Minimum7
Maximum11902
Zeros0
Zeros (%)0.0%
Memory size9.9 KiB
2021-03-06T12:30:21.441998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile12
Q11367
median6589
Q39780.75
95-th percentile11062
Maximum11902
Range11895
Interquartile range (IQR)8413.75

Descriptive statistics

Standard deviation4234.030495
Coefficient of variation (CV)0.7454245336
Kurtosis-1.658130317
Mean5680.025682
Median Absolute Deviation (MAD)4358
Skewness-0.05738117281
Sum7077312
Variance17927014.23
2021-03-06T12:30:21.521492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
11062 47 3.8%
 
10947 40 3.2%
 
7569 39 3.1%
 
11 36 2.9%
 
1413 31 2.5%
 
12 30 2.4%
 
8505 28 2.2%
 
10948 25 2.0%
 
6498 24 1.9%
 
1590 23 1.8%
 
Other values (182) 923 74.1%
 
ValueCountFrequency (%) 
7 5 0.4%
 
11 36 2.9%
 
12 30 2.4%
 
49 16 1.3%
 
56 8 0.6%
 
ValueCountFrequency (%) 
11902 8 0.6%
 
11766 4 0.3%
 
11754 1 0.1%
 
11720 2 0.2%
 
11635 1 0.1%
 

longitude
Real number (ℝ)

Distinct count1213
Unique (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.281458683215653
Minimum-6.443882
Maximum-6.055211
Zeros0
Zeros (%)0.0%
Memory size9.9 KiB
2021-03-06T12:30:21.606307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-6.443882
5-th percentile-6.4040685
Q1-6.3227085
median-6.276811362
Q3-6.23546
95-th percentile-6.166198
Maximum-6.055211
Range0.388671
Interquartile range (IQR)0.0872485

Descriptive statistics

Standard deviation0.07159854719
Coefficient of variation (CV)-0.01139839499
Kurtosis-0.09202510834
Mean-6.281458683
Median Absolute Deviation (MAD)0.0432255
Skewness-0.06818282502
Sum-7826.697519
Variance0.00512635196
2021-03-06T12:30:21.688489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-6.280268 3 0.2%
 
-6.227282 3 0.2%
 
-6.309374 3 0.2%
 
-6.255195 3 0.2%
 
-6.249849 2 0.2%
 
-6.236223 2 0.2%
 
-6.282985 2 0.2%
 
-6.264954 2 0.2%
 
-6.172943 2 0.2%
 
-6.27907 2 0.2%
 
Other values (1203) 1222 98.1%
 
ValueCountFrequency (%) 
-6.443882 1 0.1%
 
-6.443585 1 0.1%
 
-6.442693 1 0.1%
 
-6.441123 1 0.1%
 
-6.440754 1 0.1%
 
ValueCountFrequency (%) 
-6.055211 1 0.1%
 
-6.059735 1 0.1%
 
-6.060574 1 0.1%
 
-6.065742 1 0.1%
 
-6.066756 1 0.1%
 

latitude
Real number (ℝ≥0)

Distinct count1212
Unique (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.34361547308575
Minimum53.21904
Maximum53.433172
Zeros0
Zeros (%)0.0%
Memory size9.9 KiB
2021-03-06T12:30:21.779735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum53.21904
5-th percentile53.2703605
Q153.31696425
median53.345332
Q353.38013025
95-th percentile53.40117515
Maximum53.433172
Range0.214132
Interquartile range (IQR)0.063166

Descriptive statistics

Standard deviation0.04196819851
Coefficient of variation (CV)0.0007867520441
Kurtosis-0.6699352258
Mean53.34361547
Median Absolute Deviation (MAD)0.0316203
Skewness-0.3924448249
Sum66466.14488
Variance0.001761329686
2021-03-06T12:30:21.852478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
53.341431 3 0.2%
 
53.352292 3 0.2%
 
53.342931 3 0.2%
 
53.33827 3 0.2%
 
53.263563 2 0.2%
 
53.347308 2 0.2%
 
53.351807 2 0.2%
 
53.270019 2 0.2%
 
53.337848 2 0.2%
 
53.358979 2 0.2%
 
Other values (1202) 1222 98.1%
 
ValueCountFrequency (%) 
53.21904 1 0.1%
 
53.226358 1 0.1%
 
53.229695 1 0.1%
 
53.231998 1 0.1%
 
53.233244 1 0.1%
 
ValueCountFrequency (%) 
53.433172 1 0.1%
 
53.432174 1 0.1%
 
53.431643 1 0.1%
 
53.422947 1 0.1%
 
53.422316 1 0.1%
 

pricePerBedroom
Real number (ℝ)

Distinct count296
Unique (%)23.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148864.50928686082
Minimum-1500000.0
Maximum625000.0
Zeros0
Zeros (%)0.0%
Memory size9.9 KiB
2021-03-06T12:30:21.930630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1500000
5-th percentile78333.33333
Q1108333.3333
median140000
Q3187500
95-th percentile275000
Maximum625000
Range2125000
Interquartile range (IQR)79166.66667

Descriptive statistics

Standard deviation100543.2572
Coefficient of variation (CV)0.6754011264
Kurtosis81.14470276
Mean148864.5093
Median Absolute Deviation (MAD)37500
Skewness-5.624114421
Sum185485178.6
Variance1.010894658e+10
2021-03-06T12:30:22.007446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
125000 35 2.8%
 
150000 29 2.3%
 
175000 26 2.1%
 
137500 20 1.6%
 
187500 19 1.5%
 
112500 18 1.4%
 
95000 18 1.4%
 
162500 18 1.4%
 
131666.6667 18 1.4%
 
165000 18 1.4%
 
Other values (286) 1027 82.4%
 
ValueCountFrequency (%) 
-1500000 1 0.1%
 
-850000 2 0.2%
 
-745000 1 0.1%
 
-498000 1 0.1%
 
-350000 1 0.1%
 
ValueCountFrequency (%) 
625000 2 0.2%
 
600000 1 0.1%
 
497500 1 0.1%
 
460000 1 0.1%
 
450000 2 0.2%
 

deltaAvgPrice
Real number (ℝ)

HIGH CORRELATION
Distinct count792
Unique (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21526.905914686624
Minimum-1792406.779661017
Maximum477593.220338983
Zeros0
Zeros (%)0.0%
Memory size9.9 KiB
2021-03-06T12:30:22.101985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1792406.78
5-th percentile-271524.2718
Q1-41514.87395
median46190.47619
Q3110927.5701
95-th percentile276727.7977
Maximum477593.2203
Range2270000
Interquartile range (IQR)152442.444

Descriptive statistics

Standard deviation194006.1943
Coefficient of variation (CV)9.01226563
Kurtosis20.23748455
Mean21526.90591
Median Absolute Deviation (MAD)76795.41446
Skewness-2.952729132
Sum26822524.77
Variance3.763840343e+10
2021-03-06T12:30:22.178659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
68274.28571 8 0.6%
 
91384 7 0.6%
 
-33894.73684 7 0.6%
 
64732.75862 6 0.5%
 
66384 6 0.5%
 
73274.28571 6 0.5%
 
32338.23529 6 0.5%
 
282593.2203 6 0.5%
 
-83616 6 0.5%
 
43117.64706 5 0.4%
 
Other values (782) 1183 94.9%
 
ValueCountFrequency (%) 
-1792406.78 2 0.2%
 
-1592406.78 1 0.1%
 
-1321524.272 1 0.1%
 
-1065801.802 1 0.1%
 
-992661.7647 1 0.1%
 
ValueCountFrequency (%) 
477593.2203 1 0.1%
 
457593.2203 1 0.1%
 
435198.1982 1 0.1%
 
417593.2203 1 0.1%
 
412593.2203 2 0.2%
 

deltaMedianPrice
Real number (ℝ)

HIGH CORRELATION
ZEROS
Distinct count279
Unique (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-29088.28250401284
Minimum-2005000.0
Maximum350000.0
Zeros50
Zeros (%)4.0%
Memory size9.9 KiB
2021-03-06T12:30:22.265159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2005000
5-th percentile-350000
Q1-70000
median5000
Q367000
95-th percentile175000
Maximum350000
Range2355000
Interquartile range (IQR)137000

Descriptive statistics

Standard deviation195458.1566
Coefficient of variation (CV)-6.719480829
Kurtosis27.48900418
Mean-29088.2825
Median Absolute Deviation (MAD)68000
Skewness-3.831784753
Sum-36244000
Variance3.8203891e+10
2021-03-06T12:30:22.342481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0 50 4.0%
 
5000 29 2.3%
 
50000 27 2.2%
 
30000 25 2.0%
 
70000 25 2.0%
 
-50000 23 1.8%
 
25000 22 1.8%
 
60000 21 1.7%
 
15000 20 1.6%
 
-100000 19 1.5%
 
Other values (269) 985 79.1%
 
ValueCountFrequency (%) 
-2005000 2 0.2%
 
-1805000 1 0.1%
 
-1405000 1 0.1%
 
-1175000 1 0.1%
 
-1105000 2 0.2%
 
ValueCountFrequency (%) 
350000 1 0.1%
 
326000 1 0.1%
 
320000 1 0.1%
 
300000 1 0.1%
 
290000 2 0.2%
 

dublinNorthSouth
Categorical

HIGH CORRELATION
Distinct count2
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
S
652
N
594
ValueCountFrequency (%) 
S 652 52.3%
 
N 594 47.7%
 
2021-03-06T12:30:22.439348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length1
Mean length1
Min length1
ValueCountFrequency (%) 
Uppercase_Letter 2 100.0%
 
ValueCountFrequency (%) 
Latin 2 100.0%
 
ValueCountFrequency (%) 
ASCII 2 100.0%
 

distToCity
Real number (ℝ≥0)

Distinct count1216
Unique (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.952106407950893
Minimum0.09991156306907832
Maximum17.012878032749462
Zeros0
Zeros (%)0.0%
Memory size9.9 KiB
2021-03-06T12:30:22.521362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.09991156307
5-th percentile1.374030424
Q13.038546883
median5.177323871
Q38.74487627
95-th percentile12.29646768
Maximum17.01287803
Range16.91296647
Interquartile range (IQR)5.706329387

Descriptive statistics

Standard deviation3.518999816
Coefficient of variation (CV)0.5912192383
Kurtosis-0.6743279897
Mean5.952106408
Median Absolute Deviation (MAD)2.699803785
Skewness0.4964002471
Sum7416.324584
Variance12.38335971
2021-03-06T12:30:22.608603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.2065863985 3 0.2%
 
3.590435795 3 0.2%
 
2.415355904 3 0.2%
 
2.213178012 3 0.2%
 
5.229600299 2 0.2%
 
4.401617511 2 0.2%
 
1.841780665 2 0.2%
 
8.438129316 2 0.2%
 
0.4831172059 2 0.2%
 
8.239632795 2 0.2%
 
Other values (1206) 1222 98.1%
 
ValueCountFrequency (%) 
0.09991156307 1 0.1%
 
0.1835478493 1 0.1%
 
0.2065863985 3 0.2%
 
0.3172734398 1 0.1%
 
0.346479514 1 0.1%
 
ValueCountFrequency (%) 
17.01287803 1 0.1%
 
16.56325022 1 0.1%
 
16.24328956 1 0.1%
 
16.14727723 1 0.1%
 
15.94972834 1 0.1%
 

daysSincePublished
Real number (ℝ≥0)

Distinct count162
Unique (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.03531300160513
Minimum1
Maximum345
Zeros0
Zeros (%)0.0%
Memory size9.9 KiB
2021-03-06T12:30:22.694527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q142
median81
Q3109
95-th percentile145
Maximum345
Range344
Interquartile range (IQR)67

Descriptive statistics

Standard deviation45.70794704
Coefficient of variation (CV)0.593337591
Kurtosis1.887852575
Mean77.035313
Median Absolute Deviation (MAD)33
Skewness0.6046360943
Sum95986
Variance2089.216423
2021-03-06T12:30:22.783029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
61 32 2.6%
 
45 28 2.2%
 
110 28 2.2%
 
94 26 2.1%
 
81 26 2.1%
 
80 22 1.8%
 
108 21 1.7%
 
95 21 1.7%
 
46 21 1.7%
 
109 20 1.6%
 
Other values (152) 1001 80.3%
 
ValueCountFrequency (%) 
1 18 1.4%
 
2 7 0.6%
 
3 16 1.3%
 
4 10 0.8%
 
5 13 1.0%
 
ValueCountFrequency (%) 
345 1 0.1%
 
342 1 0.1%
 
309 1 0.1%
 
235 2 0.2%
 
227 1 0.1%
 

numFood
Real number (ℝ≥0)

Distinct count47
Unique (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.235955056179776
Minimum10
Maximum61
Zeros0
Zeros (%)0.0%
Memory size9.9 KiB
2021-03-06T12:30:22.873013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile29
Q142
median49
Q352
95-th percentile57
Maximum61
Range51
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.387053956
Coefficient of variation (CV)0.1813967927
Kurtosis1.078558764
Mean46.23595506
Median Absolute Deviation (MAD)4
Skewness-1.119143875
Sum57610
Variance70.34267407
2021-03-06T12:30:22.953006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
51 99 7.9%
 
50 92 7.4%
 
49 91 7.3%
 
52 82 6.6%
 
48 75 6.0%
 
47 74 5.9%
 
54 67 5.4%
 
53 56 4.5%
 
42 42 3.4%
 
55 40 3.2%
 
Other values (37) 528 42.4%
 
ValueCountFrequency (%) 
10 1 0.1%
 
13 1 0.1%
 
15 1 0.1%
 
16 1 0.1%
 
18 2 0.2%
 
ValueCountFrequency (%) 
61 2 0.2%
 
60 3 0.2%
 
59 7 0.6%
 
58 25 2.0%
 
57 29 2.3%
 

numRecreation
Real number (ℝ≥0)

Distinct count23
Unique (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.835473515248797
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Memory size9.9 KiB
2021-03-06T12:30:23.039259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q19
median12
Q315
95-th percentile18
Maximum23
Range22
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.861588419
Coefficient of variation (CV)0.3262724059
Kurtosis-0.3867217924
Mean11.83547352
Median Absolute Deviation (MAD)3
Skewness-0.1725152429
Sum14747
Variance14.91186512
2021-03-06T12:30:23.113511image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
10 142 11.4%
 
11 134 10.8%
 
9 133 10.7%
 
15 128 10.3%
 
14 96 7.7%
 
12 95 7.6%
 
16 80 6.4%
 
13 79 6.3%
 
17 73 5.9%
 
8 57 4.6%
 
Other values (13) 229 18.4%
 
ValueCountFrequency (%) 
1 3 0.2%
 
2 7 0.6%
 
3 9 0.7%
 
4 30 2.4%
 
5 40 3.2%
 
ValueCountFrequency (%) 
23 1 0.1%
 
22 1 0.1%
 
21 3 0.2%
 
20 3 0.2%
 
19 27 2.2%
 

numShop
Real number (ℝ≥0)

Distinct count39
Unique (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.406902086677368
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Memory size9.9 KiB
2021-03-06T12:30:23.192660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median7
Q318
95-th percentile27.75
Maximum39
Range38
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.245368696
Coefficient of variation (CV)0.7228403149
Kurtosis0.1792302359
Mean11.40690209
Median Absolute Deviation (MAD)2
Skewness1.072014734
Sum14213
Variance67.98610493
2021-03-06T12:30:23.278029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
5 313 25.1%
 
6 140 11.2%
 
7 70 5.6%
 
8 56 4.5%
 
9 55 4.4%
 
4 40 3.2%
 
20 37 3.0%
 
19 35 2.8%
 
21 35 2.8%
 
23 34 2.7%
 
Other values (29) 431 34.6%
 
ValueCountFrequency (%) 
1 11 0.9%
 
2 28 2.2%
 
3 25 2.0%
 
4 40 3.2%
 
5 313 25.1%
 
ValueCountFrequency (%) 
39 1 0.1%
 
38 1 0.1%
 
37 4 0.3%
 
36 3 0.2%
 
35 5 0.4%
 

Interactions

2021-03-06T12:29:51.347147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:51.464214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:51.567409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:51.677377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:51.784065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:51.892434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:52.015180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:52.116592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:52.221228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:52.324637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:52.428283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:52.570397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:52.730922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:53.004799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:53.135195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:53.266501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:53.391709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:53.509158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:53.629390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:53.761439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:53.915538image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:54.049152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:54.148955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:54.258294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:54.384432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:54.533027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:54.692551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:54.841269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:54.986430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:55.128719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:55.279410image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:55.448299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:55.605756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:55.811558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:56.048279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:56.178585image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:56.313102image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:56.423550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:56.541970image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:56.669554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:56.797934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:56.919166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:57.115533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:57.262053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:57.369742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:57.485451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:57.592526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:57.700682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:57.820123image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:57.932800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:58.057642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:58.355343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:58.527932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:58.681582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:58.836174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:58.989891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:59.149342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:59.324033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:59.489906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:59.645366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:59.808603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:29:59.973124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:00.127512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:00.292301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:00.437020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:00.567983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:00.733311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:00.848310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:00.972656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:01.137037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:01.302127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:01.470023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:01.634748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:01.785211image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:01.902312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:02.031278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:02.172277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:02.329837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:02.481794image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:02.616284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:02.746740image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:02.887171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:03.026328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:03.155065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:03.279663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:03.407189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:03.553608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:03.701353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:03.832254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:03.958650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:04.115766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:04.233653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:04.348536image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:04.459053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:04.565154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:04.674135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:04.789592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:05.073796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:05.180452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:05.292400image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:05.398612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:05.507632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:05.620524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:05.724145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:05.832665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:05.955552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:06.079652image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:06.189974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:06.304281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:06.412935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:06.525810image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:06.644598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:06.748879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:06.863912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:06.982460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:07.107512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:07.217897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:07.328014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:07.427974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:07.525048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:07.652653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:07.776482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:07.889501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:08.003178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:08.113585image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:08.224038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:08.333226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:08.429082image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:08.528423image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:08.628151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:08.727650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:08.832613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:08.943758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:09.044553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:09.142550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:09.249587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:09.351991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:09.451447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:09.555694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:09.657011image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:09.760573image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:09.872124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:09.969072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:10.074209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:10.176440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:10.276914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:10.383613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:10.490733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:10.590052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:10.686414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:10.792883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:10.907004image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:11.014427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:11.128021image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:11.241300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:11.352278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:11.712634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:11.831450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:11.940885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:12.051900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:12.165464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:12.279709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:12.393766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:12.501143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:12.606264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:12.721508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:12.831962image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:12.946100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:13.061697image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:13.173859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:13.294160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:13.414009image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:13.519531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:13.628673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:13.738709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:13.851187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:13.966296image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:14.083392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:14.192431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:14.299492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:14.416157image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:14.519718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:14.619203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:14.724326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:14.827499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:14.931462image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:15.043887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:15.144754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:15.247750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:15.347487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:15.448906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:15.556189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:15.663198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:15.764402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:15.862018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:15.968716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:16.079866image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:16.178183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:16.280020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:16.377535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:16.478390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:16.585976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:16.679557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:16.776913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:16.885100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:16.983212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:17.088090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:17.194650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:17.291571image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:17.384194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:17.487610image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:17.600070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:17.710410image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:17.826060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:17.940694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:18.061784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:18.183290image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:18.291485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:18.402028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:18.512364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:18.624041image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:18.744916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:18.861636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:18.971261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:19.079929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-03-06T12:30:23.402647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-06T12:30:23.617237image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-06T12:30:23.840139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-06T12:30:24.056898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-06T12:30:24.263143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-06T12:30:19.656656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-06T12:30:20.066916image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Sample

First rows

df_indextitleneighbourhoodpropertyTypenumBedroomsnumBathroomsfloorAreapriceratingsellerIdlongitudelatitudepricePerBedroomdeltaAvgPricedeltaMedianPricedublinNorthSouthdistToCitydaysSincePublishednumFoodnumRecreationnumShop
021 Moatfield Park, Coolock, Artane, Dublin 5Dublin 5Semi-D3.01.091380000.0E2453-6.19311753.388871126666.66666716177.5700930.0N5.85717212501711
111150 Broadford Rise, Ballinteer, Dublin 16Dublin 16Semi-D3.02.0102495000.0C349-6.26110853.277830165000.00000012338.235294-20000.0S8.366932946914
213Apartment 172, Block C, Dublin 7Dublin 7Apartment1.01.051250000.0C11331-6.27758453.348715250000.000000127430.107527100000.0N1.3874311651115
3141 Fitzgibbon Lane, Dublin 1Dublin 1Terrace1.01.060140000.0G11902-6.25660453.357853140000.000000176105.263158160000.0N0.536232157105
41518A Fitzgibbon Street, Dublin 1Dublin 1Terrace1.01.053240000.0D211902-6.25660053.357783240000.00000076105.26315860000.0N0.528618157105
51911 Elmwood Close, Clonsilla, Dublin 15Dublin 15Semi-D3.01.081270000.0D212-6.42496753.39217190000.00000073274.28571410000.0N11.890627445725
640Apartment 20, Block C, Bridge Court, Shankill, Dublin 18Dublin 18Apartment2.02.071295000.0C27425-6.12365453.231998147500.000000224395.061728130000.0S16.1472772371517
74415 The Belfry, Kilbarrack Road, Kilbarrack, Dublin 5Dublin 5Apartment2.01.059270000.0C2577-6.15275553.388560135000.000000126177.570093110000.0N8.0136024331517
8473 Castlegrange Terrace, Castaheany, Clonsilla, Dublin 15Dublin 15Semi-D3.03.0101300000.0B38666-6.43005753.397993100000.00000043274.285714-20000.0N12.449526542823
95327 Smithfield Gate, Redcow Lane, Smithfield, Dublin 1Dublin 1Apartment3.02.075350000.0D27483-6.27735353.349858116666.666667-33894.736842-50000.0N1.333301151105

Last rows

df_indextitleneighbourhoodpropertyTypenumBedroomsnumBathroomsfloorAreapriceratingsellerIdlongitudelatitudepricePerBedroomdeltaAvgPricedeltaMedianPricedublinNorthSouthdistToCitydaysSincePublishednumFoodnumRecreationnumShop
1236286610 Shamrock Cottages, North Strand, Dublin 3Dublin 3Townhouse2.01.049200000.0G2050-6.24414053.355253100000.000000278475.728155195000.0N0.9499143055125
123728705 Dun Emer Drive, Dundrum, Dublin 14Dublin 14Semi-D4.01.0138645000.0D249-6.23697653.282698161250.000000-42494.623656-50000.0S7.946910120491011
12382874133 Kiltipper Gate, Tallaght, Dublin 24Dublin 24Apartment2.02.070215000.0ZZZ8210-6.37067253.269495107500.00000066532.60869660000.0S11.9279892222419
1239287619 Beneavin Park, Glasnevin, Dublin 11Dublin 11Semi-D3.01.097370000.0D2841-6.28328953.389930123333.333333-70267.241379-88000.0N4.42285910255172
124028812 Florence Street, Portobello, Dublin 8Dublin 8Terrace3.02.0124595000.0E28456-6.26972353.331242198333.333333-253616.000000-295000.0S2.5504579552155
124128849 Saint Johns Court, Kilmore Road, Artane, Dublin 5Dublin 5Terrace2.01.071285000.0C31063-6.21893153.390851142500.000000111177.57009395000.0N4.9303233058137
12422893Apt 4, Slane House, Patrick Street, Christchurch, Dublin 8Dublin 8Apartment1.01.037230000.0E26223-6.27255253.340047230000.000000111384.00000070000.0S1.74233995695
124328994 Old Mount Pleasant, Ranelagh, Dublin 6Dublin 6Terrace3.01.0154825000.0SI_666262-6.25815853.326563275000.000000-190801.801802-300000.0S2.9489461051156
1244290210 Ard Na Greine, Eaton Brae, off Orwell Road, Rathgar, Dublin 6Dublin 6Terrace2.02.0144900000.0A39460-6.26159553.303673450000.000000-265801.801802-375000.0S5.4977945346199
124529043 Marian Drive, Rathfarnham, Dublin 14Dublin 14Detached5.03.0214850000.0C111062-6.29615753.295243170000.000000-247494.623656-255000.0S6.91020133481211